As energy resources become scare and energy conservationism prevalent, the building of smart grids has long been the policy and goals strongly supported by many governments. The smart grid is a modern electric grid integrating power generation, delivery, distribution, and the end user, which is capable of reducing power usage and increasing user energy efficiency. The first step of realizing the smart grid is extensively implementing the smart meter and constructing the Advanced Metering Infrastructure (AMI), thereby replacing the traditional manual meter reading and enhancing the power usage efficiency. According to a research report from the Environmental Change Institute of Oxford University in England, 5-15% of an electricity bill can be saved per month on average if the user is able to obtain the data of the total household electricity usage. Research from the Energy Conversation Center of Japan has also pointed out by providing real-time energy consumption information individually to small business users, spontaneous intentions and actions related to energy conservation may be triggered, thereby saving approximately 20% of energy consumption. However, the user does not know which home appliances are the main contributors of the power consumption. Therefore, if the power signatures of specific major appliances can be provided to the user, then power consumption habits can be improved while enhancing the percentage of energy savings. Although the smart meter can measure the total energy consumption state of the household, the energy consumption of individual appliances cannot be measured.
Even though as early as 1992 George W. Hart has introduced the concept of nonintrusive appliance load monitoring (NALM) to analyze the power usage of the home appliances, not many researchers were attracted to this field. Not until the recent prevalence of smart meter has rapid development of related research began. Different from intrusive appliance load monitoring, a NALM system does not require the installation of an extra device on the individual appliance to determine whether the appliance is turned on or off, but requires obtaining the load characteristic values of the individual appliance in advance in order to determine the effect of the appliance load characteristics based on the information of a single electric meter. Accordingly, the possibility of real world implementation is drastically increased, because installing an extra device on each home appliance is not a suitable solution. In conventional techniques, appliance load monitoring utilizes a power meter or a smart meter to measure the power signatures of the appliances, then compares the power signatures to identify the usage state of each appliance, so as to provide the operational state information of more appliances. However, nonintrusive load identification methods are less accurate than intrusive methods, and especially so for 220V appliances, which occupy near 50% of the household energy consumption. This has a nontrivial effect on the adoption of nonintrusive load identification. Accordingly, an urgent issue to resolve is how to effectively identify the 220V appliances and to enhance the overall identification efficiency, and thereby calculate the power consumption of the 220V appliances.
Moreover, for most of the 220V appliances (air conditioning equipments being the most representative), the characteristics of the appliances have poor repeatability and the appliances may be easily affected by environmental factors, while the variable frequency air conditioning equipments may likely to adjust their operating states automatically. These factors result in the innate difficulties of using nonintrusive monitoring methods to identify the 220V appliances, and also resulting in the accuracy degradation in identifying other 110V appliances. The power usage of the 220V appliances is made even more difficult if the automatic changes of the variable frequency air conditioners are added. However, since 220V appliances occupy near 50% of the household energy consumption, inaccurate identification thereof results in the drastic reduction of the overall identification ability.